# Augmented Neurosurgical Navigation Software Using Resting State MRI > **NIH NIH R01** · WASHINGTON UNIVERSITY · 2020 · $714,092 ## Abstract Project Summary/Abstract By providing real-time anatomic information to the neurosurgeon, stereotactic neuro-navigation has been shown to improve the extent of tumor resection and, as a result, improve survival statistics. That said, it is not routine during resections to make use of similar neuronavigation displays that reflect the functional organization of the brain. Task-based fMRI has been employed as a means of pre-operatively localizing function. However, task- based fMRI depends on the patient's ability to comply with the task paradigm, which frequently is lacking. This problem can be overcome by using the recently developed method of resting state functional magnetic resonance imaging (rsfMRI) to localize function. Moreover, rsfMRI is highly efficient, as multiple resting state networks (RSNs) associated with multiple cognitive domains can be mapped at the same time. With this in mind, the long-term goal of our research is to improve survival and quality of life after surgical resection of brain tumors by improving the identification and preservation of eloquent cortex. The current barrier that prevents the widespread use of rsfMRI is the high degree of advanced imaging expertise currently necessary to create and interpret the images. To address this shortcoming, we propose to create a turnkey system for functional mapping within the brain. At the heart of our methodology is a multi-layer perceptron (MLP) algorithm that assigns RSN membership to each locus within the brain using supervised classification of rsfMRI data. Current data demonstrate that MLP-based RSN mapping is more reliable than conventional taskbased fMRI and is extremely sensitive to sites identified by cortical stimulation, which currently is the standard in pre-surgical planning and intraoperative mapping. Translation of the science and techniques created at Washington University will be accomplished by a deep collaboration with Medtronic Corporation, the creator of the most widely used neuro- navigation system[s]. Towards this end, the overall objective of the proposed project is to create an imaging technology package that will integrate rsfMRI analysis with extant anatomical surgical stereotactic navigation. The Specific Goals of this proposal are to 1) Integrate the MLP analytic methodology into the Medtronic StealthStation Navigation System, 2) Ensure the software is stable and the output is reliable, 3) Optimize the user interface for clinical applications. The expected outcome of this translation strategy will be an integrated navigation technology using rsfMRI with clearly defined performance capabilities, well-delineated localization outputs, an intraoperatively efficient user experience, and a technical flexibility that can scale to different health care environments. Thus, this proposal is innovative because there currently does not exist any comparable system that integrates cutting edge image analysis tools with existing industry supplied clinical infrastru... ## Key facts - **NIH application ID:** 9849208 - **Project number:** 5R01CA203861-04 - **Recipient organization:** WASHINGTON UNIVERSITY - **Principal Investigator:** Eric CLAUDE Leuthardt - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $714,092 - **Award type:** 5 - **Project period:** 2017-01-17 → 2021-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9849208 ## Citation > US National Institutes of Health, RePORTER application 9849208, Augmented Neurosurgical Navigation Software Using Resting State MRI (5R01CA203861-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9849208. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*